Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
YoloV3 Implemented in Tensorflow 2.0
| Date | Stars |
|---|---|
| 2026-07-24 | 2513 |
| 2026-07-25 | 2513 |
| 2026-07-28 | 2514 |
| 2026-07-30 | 2514 |
| 2026-08-06 | 2514 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# YoloV3 Implemented in TensorFlow 2.0 [](https://colab.research.google.com/github/zzh8829/yolov3-tf2/blob/master/colab_gpu.ipynb) This repo provides a clean implementation of YoloV3 in TensorFlow 2.0 using all the best practices. ## Key Features - [x] TensorFlow 2.0 - [x] `yolov3` with pre-trained Weights - [x] `yolov3-tiny` with pre-trained Weights - [x] Inference example - [x] Transfer learning example - [x] Eager mode training with `tf.GradientTape` - [x] Graph mode training with `model.fit` - [x] Functional model with `tf.keras.layers` - [x] Input pipeline using `tf.data` - [x] Tensorflow Serving - [x] Vectorized transformations - [x] GPU accelerated - [x] Fully integrated with `absl-py` from [abseil.io](https://abseil.io) - [x] Clean implementation - [x] Following the best practices - [x] MIT License   ## Usage ### Installation #### Conda (Recommended) ```bash # Tensorflow CPU conda env create -f conda-cpu.yml conda activate yolov3-tf2-cpu # Tensorflow GPU conda env create -f conda-gpu.yml conda activate yolov3-tf2-gpu ``` #### Pip ```bash pip install -r requirements.txt ``` ### Nvidia Driver (For GPU) ```bash # Ubuntu 18.04 sudo apt-add-repository -r ppa:graphics-drivers/ppa sudo apt install nvidia-driver-430 # Windows/Other https://www.nvidia.com/Download/index.aspx ``` ### Convert pre-trained Darknet weights ```bash # yolov3 wget https://pjreddie.com/media/files/yolov3.weights -O data/yolov3.weights python convert.py --weights ./data/yolov3.weights --output ./checkpoints/yolov3.tf # yolov3-tiny wget https://pjreddie.com/media/files/yolov3-tiny.weights -O data/yolov3-tiny.weights python convert.py --weights ./data/yolov3-tiny.weights --output ./checkpoints/yolov3-tiny.tf --tiny ``` ### Detection ```bash # yolov3 python detect.py --image ./data/meme.jpg # yolov3-tiny python detect.py --weights ./checkpoints/yolov3-tiny.tf --tiny --image ./data/street.jpg # webcam python detect_video.py --video 0 # video file python detect_video.py --video path_to_file.mp4 --weights ./checkpoints/yolov3-tiny.tf --tiny # video file with output python detect_video.py --video path_to_file.mp4 --output ./output.avi ``` ### Training I have created a complete tutorial on how to train from scratch using the VOC2012 Dataset. See the documentation here https://github.com/zzh8829/yolov3-tf2/blob/master/docs/training_voc.md For customzied training, you need to generate tfrecord following the TensorFlow Object Detection API. For example you can use [Microsoft VOTT](https://github.com/Microsoft/VoTT) to generate such dataset. You can also use this [script](https://github.com/tensorflow/models/blob/master/research/object_detection/dataset_tools/create_pascal_tf_record.py) to create the pascal voc dataset. Example commend line arguments for training ``` bash python train.py --batch_size 8 --dataset ~/Data/voc2012.tfrecord --val_dataset ~/Data/voc2012_val.tfrecord --epochs 100 --mode eager_tf --transfer fine_tune python train.py --batch_size 8 --dataset ~/Data/voc2012.tfrecord --val_dataset ~/Data/voc2012_val.tfrecord --epochs 100 --mode fit --transfer none python train.py --batch_size 8 --dataset ~/Data/voc2012.tfrecord --val_dataset ~/Data/voc2012_val.tfrecord --epochs 100 --mode fit --transfer no_output python train.py --batch_size 8 --dataset ~/Data/voc2012.tfrecord --val_dataset ~/Data/voc2012_val.tfrecord --epochs 10 --mode eager_fit --transfer fine_tune --weights ./checkpoints/yolov3-tiny.tf --tiny ``` ### Tensorflow Serving You can export the model to tf serving ``` python export_tfserving.py --output serving/yolov3/1/ # verify tfserving graph saved_model_cli show --dir serving/yolov3/1/ --tag_set serve --signature_def serving_default ``` The inputs are preprocessed imag
Excerpt of 12,778 characters
Read on GitHub
Zihao Zhang · @openai · United States
71
10
Viktor Bordiuzha · Russia
1
1
1
Ehsan Rahnama
1
1
Taebum Kim · FriendliAI @friendliai · United States
1
1
1
1
1
1
1
1
1
1
1
1
Yogesh · Anna University · India
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:980c869bdb0cec09, topic:deep-learning, topic:neural-network, topic:tensorflow
matched fp:980c869bdb0cec09, topic:object-detection, topic:yolo, readme:object detection